Overview

Dataset statistics

Number of variables13
Number of observations97520
Missing cells508810
Missing cells (%)40.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 MiB
Average record size in memory80.0 B

Variable types

Numeric11
Categorical2

Alerts

p_evn_0 has constant value "manual" Constant
method has a high cardinality: 151 distinct values High cardinality
b_morn is highly correlated with b_noon and 2 other fieldsHigh correlation
t_morn is highly correlated with t_noon and 2 other fieldsHigh correlation
b_noon is highly correlated with b_morn and 2 other fieldsHigh correlation
t_noon is highly correlated with t_morn and 2 other fieldsHigh correlation
b_evn is highly correlated with b_morn and 2 other fieldsHigh correlation
t_evn is highly correlated with t_morn and 2 other fieldsHigh correlation
p_morn_0 is highly correlated with t_morn and 1 other fieldsHigh correlation
p_noon_0 is highly correlated with b_morn and 3 other fieldsHigh correlation
b_morn is highly correlated with b_noon and 1 other fieldsHigh correlation
t_morn is highly correlated with p_morn_0High correlation
b_noon is highly correlated with b_morn and 1 other fieldsHigh correlation
t_noon is highly correlated with b_evn and 2 other fieldsHigh correlation
b_evn is highly correlated with t_noon and 2 other fieldsHigh correlation
t_evn is highly correlated with t_noon and 2 other fieldsHigh correlation
p_morn_0 is highly correlated with t_morn and 1 other fieldsHigh correlation
p_noon_0 is highly correlated with b_morn and 3 other fieldsHigh correlation
b_morn is highly correlated with b_noon and 2 other fieldsHigh correlation
t_morn is highly correlated with t_noon and 2 other fieldsHigh correlation
b_noon is highly correlated with b_morn and 2 other fieldsHigh correlation
t_noon is highly correlated with t_morn and 2 other fieldsHigh correlation
b_evn is highly correlated with b_morn and 2 other fieldsHigh correlation
t_evn is highly correlated with t_morn and 2 other fieldsHigh correlation
p_morn_0 is highly correlated with t_morn and 1 other fieldsHigh correlation
p_noon_0 is highly correlated with b_morn and 3 other fieldsHigh correlation
month is highly correlated with t_morn and 1 other fieldsHigh correlation
b_morn is highly correlated with b_noon and 1 other fieldsHigh correlation
t_morn is highly correlated with month and 1 other fieldsHigh correlation
b_noon is highly correlated with b_morn and 2 other fieldsHigh correlation
t_noon is highly correlated with b_evn and 2 other fieldsHigh correlation
b_evn is highly correlated with b_noon and 2 other fieldsHigh correlation
t_evn is highly correlated with t_noon and 2 other fieldsHigh correlation
p_morn_0 is highly correlated with month and 1 other fieldsHigh correlation
p_noon_0 is highly correlated with b_morn and 1 other fieldsHigh correlation
year is highly correlated with t_noon and 1 other fieldsHigh correlation
t_morn has 69496 (71.3%) missing values Missing
b_noon has 10411 (10.7%) missing values Missing
t_noon has 69515 (71.3%) missing values Missing
t_evn has 69559 (71.3%) missing values Missing
p_morn_0 has 96424 (98.9%) missing values Missing
p_noon_0 has 96424 (98.9%) missing values Missing
p_evn_0 has 96424 (98.9%) missing values Missing

Reproduction

Analysis started2022-08-09 06:52:22.849936
Analysis finished2022-08-09 06:52:33.189428
Duration10.34 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.523010664
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.1 KiB
2022-08-09T08:52:33.220928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.448698583
Coefficient of variation (CV)0.5286973701
Kurtosis-1.208030729
Mean6.523010664
Median Absolute Deviation (MAD)3
Skewness-0.009324438533
Sum636124
Variance11.89352192
MonotonicityNot monotonic
2022-08-09T08:52:33.259849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
18277
8.5%
38277
8.5%
58277
8.5%
78277
8.5%
88277
8.5%
108277
8.5%
128277
8.5%
48010
8.2%
68010
8.2%
98010
8.2%
Other values (2)15551
15.9%
ValueCountFrequency (%)
18277
8.5%
27541
7.7%
38277
8.5%
48010
8.2%
58277
8.5%
68010
8.2%
78277
8.5%
88277
8.5%
98010
8.2%
108277
8.5%
ValueCountFrequency (%)
128277
8.5%
118010
8.2%
108277
8.5%
98010
8.2%
88277
8.5%
78277
8.5%
68010
8.2%
58277
8.5%
48010
8.2%
38277
8.5%

day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.7293991
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.1 KiB
2022-08-09T08:52:33.436176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.800036562
Coefficient of variation (CV)0.5594642559
Kurtosis-1.194007105
Mean15.7293991
Median Absolute Deviation (MAD)8
Skewness0.006779618683
Sum1533931
Variance77.44064349
MonotonicityNot monotonic
2022-08-09T08:52:33.476513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13204
 
3.3%
23204
 
3.3%
283204
 
3.3%
273204
 
3.3%
263204
 
3.3%
253204
 
3.3%
243204
 
3.3%
233204
 
3.3%
223204
 
3.3%
213204
 
3.3%
Other values (21)65480
67.1%
ValueCountFrequency (%)
13204
3.3%
23204
3.3%
33204
3.3%
43204
3.3%
53204
3.3%
63204
3.3%
73204
3.3%
83204
3.3%
93204
3.3%
103204
3.3%
ValueCountFrequency (%)
311869
1.9%
302937
3.0%
293002
3.1%
283204
3.3%
273204
3.3%
263204
3.3%
253204
3.3%
243204
3.3%
233204
3.3%
223204
3.3%

b_morn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1770
Distinct (%)1.8%
Missing172
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1008.422522
Minimum942.7000122
Maximum1054.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 KiB
2022-08-09T08:52:33.524556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum942.7000122
5-th percentile987.207568
Q11000.84827
median1009.114251
Q31016.713539
95-th percentile1027.186712
Maximum1054.5
Range111.7999878
Interquartile range (IQR)15.86526918

Descriptive statistics

Standard deviation12.18873874
Coefficient of variation (CV)0.01208693625
Kurtosis0.3585489142
Mean1008.422522
Median Absolute Deviation (MAD)7.985725037
Skewness-0.3318347085
Sum98167915.69
Variance148.5653521
MonotonicityNot monotonic
2022-08-09T08:52:33.572093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009.374196774
 
0.8%
1013.332541758
 
0.8%
1007.395061684
 
0.7%
1005.415851663
 
0.7%
1011.353331659
 
0.7%
1017.290886653
 
0.7%
1015.311676625
 
0.6%
1001.457505587
 
0.6%
1003.436716578
 
0.6%
1012.540857567
 
0.6%
Other values (1760)90800
93.1%
ValueCountFrequency (%)
942.70001221
< 0.1%
946.83258211
< 0.1%
946.90002441
< 0.1%
947.919421
< 0.1%
948.31936971
< 0.1%
949.11935051
< 0.1%
952.18574031
< 0.1%
9531
< 0.1%
953.95758851
< 0.1%
954.71880951
< 0.1%
ValueCountFrequency (%)
1054.51
< 0.1%
1054.310361
< 0.1%
10541
< 0.1%
1053.5999761
< 0.1%
1051.3000491
< 0.1%
1051.0999761
< 0.1%
1050.844021
< 0.1%
1050.8000491
< 0.1%
1049.6999511
< 0.1%
1049.51
< 0.1%

t_morn
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct497
Distinct (%)1.8%
Missing69496
Missing (%)71.3%
Infinite0
Infinite (%)0.0%
Mean10.47157045
Minimum-14
Maximum29
Zeros676
Zeros (%)0.7%
Negative3062
Negative (%)3.1%
Memory size381.1 KiB
2022-08-09T08:52:33.621676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-14
5-th percentile-3
Q13.200000048
median12.60000038
Q316.79999924
95-th percentile21.89999962
Maximum29
Range43
Interquartile range (IQR)13.59999919

Descriptive statistics

Standard deviation7.977399826
Coefficient of variation (CV)0.7618150369
Kurtosis-0.8699764013
Mean10.47157045
Median Absolute Deviation (MAD)5.649999619
Skewness-0.3435887694
Sum293455.2902
Variance63.63890457
MonotonicityNot monotonic
2022-08-09T08:52:33.666370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15909
 
0.9%
17753
 
0.8%
18717
 
0.7%
3712
 
0.7%
16707
 
0.7%
2697
 
0.7%
14692
 
0.7%
0676
 
0.7%
13616
 
0.6%
1606
 
0.6%
Other values (487)20939
 
21.5%
(Missing)69496
71.3%
ValueCountFrequency (%)
-141
 
< 0.1%
-13.100000381
 
< 0.1%
-132
 
< 0.1%
-12.52
 
< 0.1%
-1213
< 0.1%
-11.752
 
< 0.1%
-11.58
< 0.1%
-11.252
 
< 0.1%
-1112
< 0.1%
-10.751
 
< 0.1%
ValueCountFrequency (%)
293
< 0.1%
28.700000761
 
< 0.1%
28.600000381
 
< 0.1%
28.399999622
 
< 0.1%
28.299999241
 
< 0.1%
28.200000761
 
< 0.1%
286
< 0.1%
27.899999624
< 0.1%
27.799999243
< 0.1%
27.700000761
 
< 0.1%

b_noon
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1783
Distinct (%)2.0%
Missing10411
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean1008.662447
Minimum944.0530495
Maximum1054.699951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 KiB
2022-08-09T08:52:33.713210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum944.0530495
5-th percentile987.6493597
Q11001.114931
median1009.374196
Q31016.895044
95-th percentile1027.400024
Maximum1054.699951
Range110.6469017
Interquartile range (IQR)15.78011383

Descriptive statistics

Standard deviation12.13555721
Coefficient of variation (CV)0.0120313364
Kurtosis0.3982514069
Mean1008.662447
Median Absolute Deviation (MAD)7.91669056
Skewness-0.3142783609
Sum87863577.08
Variance147.2717488
MonotonicityNot monotonic
2022-08-09T08:52:33.760169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009.374196553
 
0.6%
1013.332541504
 
0.5%
1015.311676462
 
0.5%
1017.290886454
 
0.5%
1011.353331442
 
0.5%
1007.395061440
 
0.5%
1005.415851439
 
0.5%
1012.540857430
 
0.4%
1014.124225400
 
0.4%
1011.749173395
 
0.4%
Other values (1773)82590
84.7%
(Missing)10411
 
10.7%
ValueCountFrequency (%)
944.05304951
< 0.1%
9461
< 0.1%
946.436741
< 0.1%
947.38609951
< 0.1%
948.31936971
< 0.1%
950.85252031
< 0.1%
951.79998781
< 0.1%
952.45240051
< 0.1%
952.98563971
< 0.1%
953.40002441
< 0.1%
ValueCountFrequency (%)
1054.6999511
< 0.1%
1054.51
< 0.1%
1053.24381
< 0.1%
1052.9000241
< 0.1%
1051.5999761
< 0.1%
1051.4000241
< 0.1%
1050.1999511
< 0.1%
1049.910751
< 0.1%
1049.1999511
< 0.1%
10491
< 0.1%

t_noon
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct657
Distinct (%)2.3%
Missing69515
Missing (%)71.3%
Infinite0
Infinite (%)0.0%
Mean50.84488933
Minimum-12.75
Maximum1042.228288
Zeros466
Zeros (%)0.5%
Negative2231
Negative (%)2.3%
Memory size762.0 KiB
2022-08-09T08:52:33.809226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-12.75
5-th percentile-2
Q15
median14.5
Q319
95-th percentile25.5
Maximum1042.228288
Range1054.978288
Interquartile range (IQR)14

Descriptive statistics

Standard deviation193.143267
Coefficient of variation (CV)3.798676122
Kurtosis20.52971902
Mean50.84488933
Median Absolute Deviation (MAD)6.299999237
Skewness4.741124139
Sum1423911.126
Variance37304.3216
MonotonicityNot monotonic
2022-08-09T08:52:33.855872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18759
 
0.8%
19714
 
0.7%
20681
 
0.7%
17672
 
0.7%
3628
 
0.6%
15624
 
0.6%
16616
 
0.6%
2594
 
0.6%
5569
 
0.6%
1517
 
0.5%
Other values (647)21631
 
22.2%
(Missing)69515
71.3%
ValueCountFrequency (%)
-12.751
 
< 0.1%
-121
 
< 0.1%
-11.52
 
< 0.1%
-113
 
< 0.1%
-10.751
 
< 0.1%
-10.54
 
< 0.1%
-10.300000191
 
< 0.1%
-1019
< 0.1%
-9.753
 
< 0.1%
-9.57
 
< 0.1%
ValueCountFrequency (%)
1042.2282881
 
< 0.1%
1038.6658151
 
< 0.1%
1037.0825072
< 0.1%
1036.2907931
 
< 0.1%
1035.1033421
 
< 0.1%
1034.7074852
< 0.1%
1034.3116283
< 0.1%
1033.5200341
 
< 0.1%
1033.1241771
 
< 0.1%
1032.3324631
 
< 0.1%

b_evn
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1766
Distinct (%)1.8%
Missing385
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean997.4977661
Minimum0.200000003
Maximum1054.97697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 KiB
2022-08-09T08:52:33.904031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.200000003
5-th percentile986.0200418
Q11000.900024
median1009.24754
Q31016.700012
95-th percentile1027.186712
Maximum1054.97697
Range1054.77697
Interquartile range (IQR)15.79998779

Descriptive statistics

Standard deviation105.2231124
Coefficient of variation (CV)0.1054870657
Kurtosis81.43347238
Mean997.4977661
Median Absolute Deviation (MAD)7.852435586
Skewness-9.072858714
Sum96891945.51
Variance11071.90338
MonotonicityNot monotonic
2022-08-09T08:52:33.952673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009.374196829
 
0.9%
1013.332541791
 
0.8%
1005.415851684
 
0.7%
1017.290886645
 
0.7%
1007.395061621
 
0.6%
1015.311676617
 
0.6%
1011.353331610
 
0.6%
1001.457505606
 
0.6%
1010.16588583
 
0.6%
1003.436716571
 
0.6%
Other values (1756)90578
92.9%
ValueCountFrequency (%)
0.2000000031
 
< 0.1%
12.600000381
 
< 0.1%
132
 
< 0.1%
13.51
 
< 0.1%
13.600000381
 
< 0.1%
13.699999812
 
< 0.1%
13.800000191
 
< 0.1%
13.899999624
< 0.1%
148
< 0.1%
14.100000383
 
< 0.1%
ValueCountFrequency (%)
1054.976971
< 0.1%
1054.9000241
< 0.1%
1054.5999761
< 0.1%
1052.3000491
< 0.1%
1051.8000491
< 0.1%
1051.1999511
< 0.1%
10501
< 0.1%
1049.8000491
< 0.1%
1049.5999761
< 0.1%
1048.844191
< 0.1%

t_evn
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct627
Distinct (%)2.2%
Missing69559
Missing (%)71.3%
Infinite0
Infinite (%)0.0%
Mean166.3063633
Minimum-13
Maximum4116.083043
Zeros643
Zeros (%)0.7%
Negative2700
Negative (%)2.8%
Memory size762.0 KiB
2022-08-09T08:52:34.001270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-13
5-th percentile-2.5
Q14
median14
Q318
95-th percentile24
Maximum4116.083043
Range4129.083043
Interquartile range (IQR)14

Descriptive statistics

Standard deviation769.6894262
Coefficient of variation (CV)4.628141767
Kurtosis20.56565316
Mean166.3063633
Median Absolute Deviation (MAD)6
Skewness4.749489999
Sum4650092.225
Variance592421.8128
MonotonicityNot monotonic
2022-08-09T08:52:34.049053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18823
 
0.8%
16760
 
0.8%
17758
 
0.8%
2721
 
0.7%
15703
 
0.7%
3675
 
0.7%
19665
 
0.7%
0643
 
0.7%
20614
 
0.6%
5611
 
0.6%
Other values (617)20988
 
21.5%
(Missing)69559
71.3%
ValueCountFrequency (%)
-131
 
< 0.1%
-12.51
 
< 0.1%
-123
 
< 0.1%
-11.51
 
< 0.1%
-11.253
 
< 0.1%
-1114
< 0.1%
-10.753
 
< 0.1%
-10.53
 
< 0.1%
-10.300000191
 
< 0.1%
-10.251
 
< 0.1%
ValueCountFrequency (%)
4116.0830431
< 0.1%
4101.981121
< 0.1%
4097.2807981
< 0.1%
4095.7138652
< 0.1%
4087.8796762
< 0.1%
4086.3127432
< 0.1%
4084.7462872
< 0.1%
4083.1793541
< 0.1%
4080.0454871
< 0.1%
4076.9120982
< 0.1%

method
Categorical

HIGH CARDINALITY

Distinct151
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size762.0 KiB
manual
94598 
automatic
 
1826
1012.5408721238651
 
28
1006.9991736322698
 
26
1006.2075196761349
 
25
Other values (146)
 
1017

Length

Max length18
Median length6
Mean length6.176251025
Min length6

Characters and Unicode

Total characters602308
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)< 0.1%

Sample

1st row1014.9158339922699
2nd row1019.6658785277301
3rd row997.49920536
4th row1024.0200658854603
5th row1019.27002135

Common Values

ValueCountFrequency (%)
manual94598
97.0%
automatic1826
 
1.9%
1012.540872123865128
 
< 0.1%
1006.999173632269826
 
< 0.1%
1006.207519676134925
 
< 0.1%
1011.749218167730224
 
< 0.1%
1010.16584985613523
 
< 0.1%
1009.374195899999922
 
< 0.1%
1013.3325260822
 
< 0.1%
1012.9366689022720
 
< 0.1%
Other values (141)906
 
0.9%

Length

2022-08-09T08:52:34.094801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manual94598
97.0%
automatic1826
 
1.9%
1012.540872123865128
 
< 0.1%
1006.999173632269826
 
< 0.1%
1006.207519676134925
 
< 0.1%
1011.749218167730224
 
< 0.1%
1010.16584985613523
 
< 0.1%
1009.374195899999922
 
< 0.1%
1013.3325260822
 
< 0.1%
1007.790887987730120
 
< 0.1%
Other values (141)906
 
0.9%

Most occurring characters

ValueCountFrequency (%)
a192848
32.0%
m96424
16.0%
u96424
16.0%
n94598
15.7%
l94598
15.7%
t3652
 
0.6%
02462
 
0.4%
12449
 
0.4%
92110
 
0.4%
i1826
 
0.3%
Other values (10)14917
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter584022
97.0%
Decimal Number17190
 
2.9%
Other Punctuation1096
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02462
14.3%
12449
14.2%
92110
12.3%
21617
9.4%
71570
9.1%
31555
9.0%
61432
8.3%
51431
8.3%
81421
8.3%
41143
6.6%
Lowercase Letter
ValueCountFrequency (%)
a192848
33.0%
m96424
16.5%
u96424
16.5%
n94598
16.2%
l94598
16.2%
t3652
 
0.6%
i1826
 
0.3%
c1826
 
0.3%
o1826
 
0.3%
Other Punctuation
ValueCountFrequency (%)
.1096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin584022
97.0%
Common18286
 
3.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02462
13.5%
12449
13.4%
92110
11.5%
21617
8.8%
71570
8.6%
31555
8.5%
61432
7.8%
51431
7.8%
81421
7.8%
41143
6.3%
Latin
ValueCountFrequency (%)
a192848
33.0%
m96424
16.5%
u96424
16.5%
n94598
16.2%
l94598
16.2%
t3652
 
0.6%
i1826
 
0.3%
c1826
 
0.3%
o1826
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII602308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a192848
32.0%
m96424
16.0%
u96424
16.0%
n94598
15.7%
l94598
15.7%
t3652
 
0.6%
02462
 
0.4%
12449
 
0.4%
92110
 
0.4%
i1826
 
0.3%
Other values (10)14917
 
2.5%

p_morn_0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct114
Distinct (%)10.4%
Missing96424
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean18.14543794
Minimum12
Maximum25.60000038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.1 KiB
2022-08-09T08:52:34.138446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile15
Q116.10000038
median17.60000038
Q320
95-th percentile22.70000076
Maximum25.60000038
Range13.60000038
Interquartile range (IQR)3.899999619

Descriptive statistics

Standard deviation2.526624918
Coefficient of variation (CV)0.1392429836
Kurtosis-0.621186018
Mean18.14543794
Median Absolute Deviation (MAD)1.600000381
Skewness0.5366756916
Sum19887.39998
Variance6.383832932
MonotonicityNot monotonic
2022-08-09T08:52:34.189530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1760
 
0.1%
1654
 
0.1%
1838
 
< 0.1%
15.8999996232
 
< 0.1%
16.8999996232
 
< 0.1%
2230
 
< 0.1%
17.8999996230
 
< 0.1%
1526
 
< 0.1%
16.7999992425
 
< 0.1%
16.2000007623
 
< 0.1%
Other values (104)746
 
0.8%
(Missing)96424
98.9%
ValueCountFrequency (%)
121
 
< 0.1%
12.100000381
 
< 0.1%
12.899999621
 
< 0.1%
132
< 0.1%
13.100000381
 
< 0.1%
13.199999811
 
< 0.1%
13.399999621
 
< 0.1%
13.51
 
< 0.1%
13.699999811
 
< 0.1%
13.800000193
< 0.1%
ValueCountFrequency (%)
25.600000381
 
< 0.1%
24.799999241
 
< 0.1%
24.52
 
< 0.1%
24.200000761
 
< 0.1%
245
< 0.1%
23.899999623
< 0.1%
23.799999243
< 0.1%
23.700000762
 
< 0.1%
23.600000382
 
< 0.1%
23.51
 
< 0.1%

p_noon_0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct148
Distinct (%)13.5%
Missing96424
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean253.8802919
Minimum243.8000031
Maximum262.6000061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.1 KiB
2022-08-09T08:52:34.239718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum243.8000031
5-th percentile249
Q1252
median254
Q3255.6000061
95-th percentile258.8999939
Maximum262.6000061
Range18.80000305
Interquartile range (IQR)3.600006104

Descriptive statistics

Standard deviation2.929549217
Coefficient of variation (CV)0.01153909661
Kurtosis0.1323265582
Mean253.8802919
Median Absolute Deviation (MAD)1.800003052
Skewness-0.1188405529
Sum278252.8
Variance8.582259178
MonotonicityNot monotonic
2022-08-09T08:52:34.290329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253.600006125
 
< 0.1%
254.399993924
 
< 0.1%
255.199996924
 
< 0.1%
255.600006122
 
< 0.1%
253.800003122
 
< 0.1%
255.399993922
 
< 0.1%
255.300003121
 
< 0.1%
251.399993921
 
< 0.1%
25319
 
< 0.1%
253.399993918
 
< 0.1%
Other values (138)878
 
0.9%
(Missing)96424
98.9%
ValueCountFrequency (%)
243.80000311
 
< 0.1%
2451
 
< 0.1%
245.69999692
< 0.1%
245.89999391
 
< 0.1%
246.10000611
 
< 0.1%
246.19999692
< 0.1%
246.30000313
< 0.1%
246.39999391
 
< 0.1%
246.60000612
< 0.1%
246.80000313
< 0.1%
ValueCountFrequency (%)
262.60000611
 
< 0.1%
261.70001221
 
< 0.1%
261.51
 
< 0.1%
261.39999393
< 0.1%
2611
 
< 0.1%
260.89999394
< 0.1%
260.79998781
 
< 0.1%
260.60000612
< 0.1%
260.51
 
< 0.1%
260.39999391
 
< 0.1%

p_evn_0
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.1%
Missing96424
Missing (%)98.9%
Memory size762.0 KiB
manual
1096 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6576
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmanual
2nd rowmanual
3rd rowmanual
4th rowmanual
5th rowmanual

Common Values

ValueCountFrequency (%)
manual1096
 
1.1%
(Missing)96424
98.9%

Length

2022-08-09T08:52:34.335668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-09T08:52:34.369908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
manual1096
100.0%

Most occurring characters

ValueCountFrequency (%)
a2192
33.3%
m1096
16.7%
n1096
16.7%
u1096
16.7%
l1096
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6576
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2192
33.3%
m1096
16.7%
n1096
16.7%
u1096
16.7%
l1096
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin6576
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2192
33.3%
m1096
16.7%
n1096
16.7%
u1096
16.7%
l1096
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2192
33.3%
m1096
16.7%
n1096
16.7%
u1096
16.7%
l1096
16.7%

year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct262
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1888.906501
Minimum1756
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.1 KiB
2022-08-09T08:52:34.403932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1756
5-th percentile1769
Q11822
median1889
Q31956
95-th percentile2009
Maximum2017
Range261
Interquartile range (IQR)134

Descriptive statistics

Standard deviation76.92028478
Coefficient of variation (CV)0.04072212401
Kurtosis-1.208119673
Mean1888.906501
Median Absolute Deviation (MAD)67
Skewness-0.006556520474
Sum184206162
Variance5916.730211
MonotonicityNot monotonic
2022-08-09T08:52:34.454089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2016732
 
0.8%
2015730
 
0.7%
2017730
 
0.7%
2013730
 
0.7%
2014730
 
0.7%
1980366
 
0.4%
1912366
 
0.4%
2004366
 
0.4%
2000366
 
0.4%
1952366
 
0.4%
Other values (252)92038
94.4%
ValueCountFrequency (%)
1756366
0.4%
1757365
0.4%
1758365
0.4%
1759365
0.4%
1760366
0.4%
1761365
0.4%
1762365
0.4%
1763365
0.4%
1764366
0.4%
1765365
0.4%
ValueCountFrequency (%)
2017730
0.7%
2016732
0.8%
2015730
0.7%
2014730
0.7%
2013730
0.7%
2012366
0.4%
2011365
0.4%
2010365
0.4%
2009365
0.4%
2008366
0.4%

Interactions

2022-08-09T08:52:32.097547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:26.654320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.244756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.792224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.314382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.786349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.493852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.012566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.537387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.990357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.607702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.145311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:26.721834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.294674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.845711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.357642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.028919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.547281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.065145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.579339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.172765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.649521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.195226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:26.796397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.348985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.896930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.400397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.079984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.594142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.123240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.621652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.215372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.692587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.239089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:26.865562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.394491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.942206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.441233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.125379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.639119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.172282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.661572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.259843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.737272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.287621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:26.926549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.445733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.989952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.482685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.172259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.682395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.222638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.701928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.304659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.783461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.329692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:26.971944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.489713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.035154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.522338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.216647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.725824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.269618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.743250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.348498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.827790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.380647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.019340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.538926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.084066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.565208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.264433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.771181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.319723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.783284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.392832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.872173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.423504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.063583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.582005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.127057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.606329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.308385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.817660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.363356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.824387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.433536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.913213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.464005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.103280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.622746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.165382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.643032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.346553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.860467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.402492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.861693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.474436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.955164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.511940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.145765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.665282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.209528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.688030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.391732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.907128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.444655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.903374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.516933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.999533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.568079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.195925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:27.731401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.267107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:28.736025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.447190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:29.957799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.495455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:30.948504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:31.561068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-09T08:52:32.043790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-09T08:52:34.499473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-09T08:52:34.566992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-09T08:52:34.632185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-09T08:52:34.828046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-09T08:52:32.651800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-09T08:52:32.870387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-09T08:52:33.073526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-09T08:52:33.144532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

monthdayb_mornt_mornb_noont_noonb_evnt_evnmethodp_morn_0p_noon_0p_evn_0year
0111018.47830713.01017.2908561017.68671318.0000004018.9389091014.915833992269917.900000255.899994manual1859
1121009.37419615.11007.7908881011.35336115.9000003995.4363431019.665878527730116.000000257.100006manual1859
2131018.47830715.51016.4991421008.18668516.2999993982.901592997.4992053615.900000251.500000manual1859
3141009.37419614.31007.7908881015.70754813.9000004014.2385871024.020065885460317.200001258.200012manual1859
4151021.24918615.01019.6658791019.66587916.7999994028.3400321019.2700213518.000000256.899994manual1859
5161011.74921815.71009.7700531006.20752019.6000003971.9337761006.207519676134918.299999253.500000manual1859
617995.52004016.6993.5408751002.24918914.0000003960.9659591012.9366689022716.100000255.399994manual1859
7181025.20751716.01023.2283521027.97839618.9000004059.6767871027.582538887730217.000000259.100006manual1859
8191021.64504415.11019.6658791016.10340618.7000014012.6716541012.540872123865117.100000255.300003manual1859
9110999.47837016.0997.895063991.56171019.0000003915.527520978.895065593865117.000000246.800003manual1859

Last rows

monthdayb_mornt_mornb_noont_noonb_evnt_evnmethodp_morn_0p_noon_0p_evn_0year
9751012221024.020051NaNNaNNaN1022.436682NaNmanualNaNNaNNone1763
9751112231014.519992NaNNaNNaN1008.978354NaNmanualNaNNaNNone1763
975121224997.499236NaNNaNNaN991.561680NaNmanualNaNNaNNone1763
975131225990.374229NaNNaNNaN987.603410NaNmanualNaNNaNNone1763
975141226994.332574NaNNaNNaN1005.020009NaNmanualNaNNaNNone1763
975151227999.478370NaNNaNNaN989.186703NaNmanualNaNNaNNone1763
975161228995.915867NaNNaNNaN1011.353331NaNmanualNaNNaNNone1763
9751712291004.228400NaNNaNNaN986.020042NaNmanualNaNNaNNone1763
975181230967.020075NaNNaNNaN953.957588NaNmanualNaNNaNNone1763
975191231962.270046NaNNaNNaN977.311743NaNmanualNaNNaNNone1763